This disclosure concerns estimating the number of people within building-based device networks. Increasingly, local area network (LAN) devices are capable of sensing people (e.g. smart appliances). Features of building layout, and physical arrangement of devices provide that only a subset of sensor data patterns can be accounted for by a single user (e.g. simultaneous activation of a smart TV and a motion sensor). Over time, a central controller (e.g. LAN gateway) can gather these building-specific sensor patterns and learn single occupant characteristics for a network (e.g. features or boundaries of single occupant capability). In one embodiment, the central controller generates a single occupancy criterion (e.g. a building-specific test), comprising single occupant characteristics, operable to test a subset of sensor data for single occupancy. The central controller divides devices into a minimum number of groups, such that each group satisfies the single occupancy criterion and estimates the number of occupants accordingly.
Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A computer implemented method for estimating the number of people in a region within an enclosed space comprising: obtaining a set of sensor data from a plurality of devices within a network of devices, wherein the set of sensor data comprises for each device in the plurality of devices corresponding sensor data indicative of a measure of occupancy within at least a portion of the region, obtaining a single occupancy criterion comprising a plurality of single occupant characteristics, wherein the single occupancy criterion functions to identify whether a candidate subset of the set of sensor data can be accounted for by a single occupant, wherein the single occupancy criterion further comprises a test based on the plurality of single occupancy characteristics, and wherein the single occupancy criterion is satisfied by the candidate subset of the set of sensor data when the candidate subset passes the test, processing one or more subsets of the set of sensor data according to the single occupancy criterion, wherein the processing identifies a set of subsets of devices such that for each subset of devices in the set of subsets of devices the corresponding subset of the set of sensor data satisfies the single occupancy criterion, and such that each device in the plurality of devices is included in at least one subset of devices in the set of subsets of devices, and estimating the number of people in the region within the enclosed space according to the number of subsets of devices in the set of subsets of devices.
A computer system estimates the number of people in a room by analyzing sensor data from devices (like smart appliances) on a network. The system uses a "single occupancy criterion" – a set of rules that determines if a subset of sensor data could be caused by just one person. This criterion includes a test based on single-occupant characteristics. The system divides the devices into the fewest possible groups, where each group's sensor data satisfies the single occupancy criterion (meaning one person could be responsible for that group's activity). The number of groups then provides an estimate of the number of people in the room. Every device must belong to at least one group.
2. The method of claim 1 : wherein each of the plurality of single occupant characteristics is a rule operable to be evaluated on the candidate subset of the set of sensor data and which when satisfied by the candidate subset of the set sensor data indicates that the candidate subset of the set of sensor data can be accounted for by a single occupant, and wherein at least one of the plurality of single occupant characteristics is generated by a computer and based on processing training data from at least one of the plurality of devices by the computer.
In the method for estimating the number of people in a room (where a computer system analyzes sensor data from networked devices and uses a "single occupancy criterion" to group devices and estimate the population), the "single occupancy criterion" includes rules evaluated against sensor data. If the rules are satisfied, it indicates that a single person could account for the device activity. At least one of these rules is generated by a computer that analyzes training data from the devices to learn typical single-person behavior patterns.
3. The method of claim 1 wherein the single occupancy criterion comprises at least one single occupant characteristic that is a concurrent characteristic and at least one single occupant characteristic that is a sequential characteristic.
In the method for estimating the number of people in a room (where a computer system analyzes sensor data from networked devices and uses a "single occupancy criterion" to group devices and estimate the population), the single occupancy criterion includes both concurrent characteristics (things happening at the same time) and sequential characteristics (things happening in a certain order) to better identify single-person activity patterns.
4. The method of claim 1 wherein at least one single occupant characteristic describes a minimum transition time between at least two devices from the plurality of devices that can be accounted for by a single occupant.
In the method for estimating the number of people in a room (where a computer system analyzes sensor data from networked devices and uses a "single occupancy criterion" to group devices and estimate the population), at least one of the single occupant characteristics describes the minimum time it would take for one person to move between and activate two different devices.
5. The method of claim 1 wherein at least one of the plurality of single occupant characteristics is generated at least in part by a computer coupled to the network of devices.
In the method for estimating the number of people in a room (where a computer system analyzes sensor data from networked devices and uses a "single occupancy criterion" to group devices and estimate the population), at least one of the single occupant characteristics is automatically generated (at least in part) by a computer connected to the network of devices.
6. The method of claim 1 : wherein the processing identifies the set of subsets of devices by iteratively performing, the steps of: a. generating a new subset of devices comprising the largest possible number of devices from the plurality of devices that are not already included in a subset of devices in the set of subsets of devices, and such that the single occupancy criterion is satisfied by the corresponding subset of the set of sensor data for all devices in the new subset of devices, b. adding the new subset of devices to the set of subsets of devices.
In the method for estimating the number of people in a room (where a computer system analyzes sensor data from networked devices and uses a "single occupancy criterion" to group devices and estimate the population), the system creates device groups iteratively. It starts by creating a new group with the largest possible number of devices not already in a group, ensuring the single occupancy criterion is met for that group's sensor data. It then adds this new group to the overall set of groups and repeats until all devices belong to a group.
7. The method of claim 1 wherein each of the plurality of single occupant characteristics defines how to generate a corresponding score when evaluated on the candidate subset of the set of sensor data, and wherein the test defines a scoring criterion for passing the test, the scoring criterion using the score corresponding to at least one of the plurality of single occupancy characteristics.
In the method for estimating the number of people in a room (where a computer system analyzes sensor data from networked devices and uses a "single occupancy criterion" to group devices and estimate the population), each single occupant characteristic has a way of calculating a score when checked against sensor data. The test within the single occupancy criterion defines a scoring system: to "pass" the test (and thus be considered single-occupant activity), a certain score (based on at least one single occupant characteristic) must be achieved.
8. The method of claim 1 wherein for each of the plurality of devices the corresponding sensor data is obtained in short range wireless signals having a range of less than 800 meters.
In the method for estimating the number of people in a room (where a computer system analyzes sensor data from networked devices and uses a "single occupancy criterion" to group devices and estimate the population), the sensor data from each device is gathered using short-range wireless signals (less than 800 meters).
9. The method of claim 1 wherein the network of devices is a local area network and wherein the processing to identify the set of subsets of devices is performed by a computer that is part of the local area network.
In the method for estimating the number of people in a room (where a computer system analyzes sensor data from networked devices and uses a "single occupancy criterion" to group devices and estimate the population), the devices are on a local area network (LAN), and the computer performing the device grouping is also part of the LAN.
10. The method of claim 1 further comprising: estimating the number of people in the region according to the number of subsets of devices in the set of subsets of devices and according to the number of subsets of devices in the set of subsets of devices at an earlier time.
The method for estimating the number of people in a room (where a computer system analyzes sensor data from networked devices and uses a "single occupancy criterion" to group devices and estimate the population) refines its estimate by also considering previous estimates of the number of people present.
11. The method of claim 1 wherein the test in the single occupancy criterion is based on at least four single occupant characteristics.
In the method for estimating the number of people in a room (where a computer system analyzes sensor data from networked devices and uses a "single occupancy criterion" to group devices and estimate the population), the single occupancy criterion's test relies on at least four different single occupant characteristics to ensure sufficient accuracy.
12. The method of claim 1 further comprising: prior to generating the set of subsets of devices, varying a detection threshold to generate a varied detection threshold, wherein the varied detection threshold is based at least in part on a previous estimate of the number of people in the region, and processing at least some of the sensor data according to the varied detection threshold.
The method for estimating the number of people in a room (where a computer system analyzes sensor data from networked devices and uses a "single occupancy criterion" to group devices and estimate the population) adjusts the sensitivity of device detection thresholds before creating device groups. This adjustment is based, at least in part, on prior estimates of the number of people in the room.
13. The method of claim 1 wherein at least one of the plurality of single occupant characteristics is computed based on processing training data by a computer coupled to gather the training data form one or more of the plurality of devices upon satisfaction of a training criterion, and wherein the training criterion comprises a condition for the satisfaction of the training criterion.
In the method for estimating the number of people in a room (where a computer system analyzes sensor data from networked devices and uses a "single occupancy criterion" to group devices and estimate the population), at least one single occupant characteristic is based on training data gathered from the devices. This training data is collected only when a specific "training criterion" is met (a defined condition being satisfied).
14. The method of claim 2 wherein at least one single occupant characteristic in the single occupancy criterion is a rule indicating one or more combinations of sensor data that cannot be accounted for by a single occupant and thereby defining one or more combinations of sensor data that cannot satisfy the single occupancy criterion.
In the method for estimating the number of people in a room (where a computer system analyzes sensor data from networked devices and uses a "single occupancy criterion" to group devices and estimate the population, and where at least one single occupant characteristic is a rule), at least one single occupant characteristic identifies combinations of sensor data that are impossible for a single person to generate (and thus would automatically fail the single occupancy criterion).
15. The method of claim 2 wherein the test is passed by the candidate subset of the set of sensor data when each of the plurality of single occupant characteristics are satisfied by the candidate subset of the set of sensor data.
In the method for estimating the number of people in a room (where a computer system analyzes sensor data from networked devices and uses a "single occupancy criterion" to group devices and estimate the population, and where at least one single occupant characteristic is a rule), a subset of sensor data "passes" the single occupancy test only if it satisfies ALL of the defined single occupant characteristics.
16. The method of claim 2 further comprising: estimating the number of people in the region according the number of subsets of devices in the set of subsets of devices and a previous estimate of the number of people in the region.
The method for estimating the number of people in a room (where a computer system analyzes sensor data from networked devices and uses a "single occupancy criterion" to group devices and estimate the population, and where at least one single occupant characteristic is a rule) refines its estimate by also considering previous estimates of the number of people present.
17. A non-transitory computer readable medium containing instructions stored therein for causing a computer processor to perform a method for estimating the number of people in a region of an enclosed space, comprising: instructions for obtaining sensor data from a plurality of devices within a network of devices, the sensor data being indicative of occupancy within at least a portion of the region, instructions for obtaining a single occupancy criterion comprising a plurality of single occupant characteristics each being a rule which when satisfied by a candidate subset of the sensor data corresponding to a candidate set of devices from the plurality of devices indicates that the candidate subset of the sensor data can be accounted for by a single occupant, wherein the single occupancy criterion is satisfied by the candidate subset of the sensor data when a combination of the plurality of single occupant characteristics is satisfied by the candidate subset of the sensor data, wherein the combination is defined in the single occupancy criterion, instructions for processing one or more subsets of the sensor data according to the single occupancy criterion, to identify a set of subsets of devices such that for each subset of devices in the set of subsets of devices the corresponding subset of the sensor data satisfies the single occupancy criterion, and such that each device in the plurality of devices is included in at least one subset of devices in the set of subsets of devices, and instructions for estimating the number of people in the region of the enclosed space based on the number of subsets of devices in the set of subsets of devices.
A non-transitory computer-readable medium (like a hard drive or flash drive) stores instructions that, when executed, cause a computer to estimate the number of people in a room. The instructions include: acquiring sensor data from networked devices indicative of occupancy, using a "single occupancy criterion" comprised of rules determining if sensor data can be caused by one person, grouping devices into subsets where each subset satisfies the single occupancy criterion and all devices belong to at least one subset, and estimating the number of people based on the number of device subsets.
18. The non-transitory computer readable medium of claim 17 further comprising: instructions for obtaining occupancy data indicative of occupancy in the region, instructions for evaluating a training criterion on the occupancy data to generate a result indicating the number of people in the region, and based at least in part on the result, obtaining training data from one or more of the plurality of devices, instructions for processing the training data to generate a single occupant characteristic, and instructions for adding the single occupant characteristic to the plurality of single occupant characteristics in the single occupancy criterion.
The computer readable medium for estimating the number of people in a room (using instructions for obtaining sensor data, a single occupancy criterion, grouping devices, and estimating population), further includes instructions to obtain separate occupancy data, use this data to determine the actual number of people, collect training data from the devices based on this determined number, process the training data to create a new single occupant characteristic (rule), and add this new rule to the existing single occupancy criterion.
19. The non-transitory computer readable medium of claim 17 further comprising: instructions for generating the at least one of the plurality of single occupant characteristics by processing training data from the plurality of devices.
The computer readable medium for estimating the number of people in a room (using instructions for obtaining sensor data, a single occupancy criterion, grouping devices, and estimating population), further includes instructions to generate at least one of the single occupant characteristic rules by processing training data from the devices.
20. The non-transitory computer readable medium of claim 17 further comprising: instructions for estimating the number of people of the region in the enclosed space based on the number of subsets of devices in the set of subsets of devices and based on a previous estimate of the number of people in the region of the enclosed space.
The computer readable medium for estimating the number of people in a room (using instructions for obtaining sensor data, a single occupancy criterion, grouping devices, and estimating population), further includes instructions to refine the population estimate by considering both the number of device subsets and a previous population estimate.
21. A central controller comprising: at least one processor; and memory storing computer-readable instructions that, when executed by the at least one processor, cause the central controller to: obtain sensor data from a plurality of devices, wherein the sensor data from each device of the plurality of devices is indicative of a measure of occupancy within at least a portion of a region in an enclosed space, access a single occupancy criterion, comprising a test based on a plurality of single occupant characteristics, and which when satisfied by a candidate subset of the sensor data corresponding to a candidate subset of devices from the plurality of devices indicates that the candidate subset of the sensor data can be accounted for by a single occupant, wherein the single occupancy criterion is satisfied for the candidate subset of the sensor data when the test is passed by the candidate subset of the sensor data, process the single occupancy criterion using one or more subsets of devices from the plurality of devices, to generate a set of subsets of devices such that for each subset of devices in the set of subsets of devices the corresponding subset of the sensor data from all devices in the each subset of devices satisfies the single occupancy criterion, and estimate the number of people in the region in the enclosed space based on the number of subsets of devices in the set of subsets of devices.
A central controller device (like a smart home hub) includes a processor and memory. The memory stores instructions that, when executed by the processor, cause the device to: obtain sensor data from networked devices indicating occupancy, use a "single occupancy criterion" with a test to determine if sensor data can be caused by one person, group devices into subsets where each subset satisfies the single occupancy criterion, and estimate the number of people based on the number of device subsets.
22. The central controller of claim 21 wherein the single occupancy criterion comprises at least one single occupant characteristic that is generated at least in part by processing training data form at least one of the plurality of devices at the central controller.
The central controller (which estimates population using sensor data, a single occupancy criterion, and device grouping) generates at least one single occupant characteristic rule by processing training data from the devices directly at the central controller.
23. The central controller of claim 21 wherein the computer-readable instructions, when executed by the at least one processor, further cause the central controller to compute the set of subsets of devices by: iteratively performing, until all devices in the plurality of devices are included in a subset in the set of subsets of devices, the steps of: a. generating a largest possible subset of devices comprising devices from the plurality of devices that are not already included in a subset of devices in the set of subset of devices and such that the single occupancy criterion is satisfied by the combined sensor data for all devices in the largest possible subset, b. adding the largest possible subset to the set of subsets of devices.
The central controller (which estimates population using sensor data, a single occupancy criterion, and device grouping) determines the device subsets iteratively. It creates the largest possible subset of devices not already in a group, ensuring that the combined sensor data satisfies the single occupancy criterion. It then adds this subset to the overall group set and repeats until all devices are in a subset.
24. The central controller of claim 21 wherein at least one of the single occupant characteristics is based processing training data by a computer operable coupled to receive the training data from one or more of the plurality of devices.
In the central controller (which estimates population using sensor data, a single occupancy criterion, and device grouping), at least one single occupant characteristic is derived by processing training data using a computer that is connected to and receives training data from the devices.
25. The central controller of claim 21 wherein at least one of the single occupant characteristics is based on processing training data by the central controller.
In the central controller (which estimates population using sensor data, a single occupancy criterion, and device grouping), at least one single occupant characteristic is created by the central controller itself through processing training data.
26. The central controller of claim 21 wherein the central controller is located in a local area network controller and wherein the sensor data for at least some of the plurality of devices is gathered from data packets transmitted by the at least some of the plurality of devices through the local area network controller.
The central controller (which estimates population using sensor data, a single occupancy criterion, and device grouping) is located within a local area network (LAN) controller, and the device sensor data is obtained from data packets transmitted by the devices through the LAN controller.
27. A computer implemented method for estimating the number of people in a region of an enclosed space comprising: obtaining a set of sensor data, indicative of occupancy within at least some of the region, and comprising for each of a plurality of devices within a network of devices corresponding sensor data, obtaining a single occupancy criterion comprising a plurality of single occupant characteristics, wherein upon processing at least some of the set of sensor data according to the single occupancy criterion, the single occupancy criterion functions to identify whether the at least some of the set of sensor data can be accounted for by a single occupant, wherein the single occupancy criterion further comprises a set of rules for the satisfaction of the single occupancy criterion, the set of rules being based at least in part on at least some of the plurality of single occupancy characteristics processing one or more subsets of the set of sensor data according to the single occupancy criterion, to compute a set of subsets of devices such that for each subset of devices in the set of subsets of devices the corresponding subset of the set of sensor data satisfies the single occupancy criterion, and such that each device in the plurality of devices is included in at least one subset of devices in the set of subsets of devices, and estimating the number of people in the region of the enclosed space based on the number of subsets of devices in the set of subsets of devices.
A computer method estimates the number of people in a room by getting sensor data from devices, using a "single occupancy criterion" (rules for determining if data can be caused by one person), and grouping devices into subsets where each subset satisfies this criterion. The final population estimate is based on the number of device subsets. The single occupancy criterion uses a set of rules derived from multiple single occupant characteristics.
28. The method of claim 27 wherein one or more of the set of rules are generated at least in part by processing training data by a computer operably coupled to receive the training data from the network of devices.
In the method for estimating the number of people in a room (where sensor data is analyzed using a single occupancy criterion to group devices and estimate population), one or more rules used in the single occupancy criterion are automatically generated by a computer analyzing training data from the network of devices.
29. The method of claim 27 wherein at least one of the set of rules is based on processing training data from a second network of devices located in a second enclosed space.
In the method for estimating the number of people in a room (where sensor data is analyzed using a single occupancy criterion to group devices and estimate population), at least one rule in the single occupancy criterion is based on training data collected from a completely separate network of devices located in a different room.
30. The method of claim 27 further comprising the steps of, prior to computing the set of subsets of devices, modifying one or more of the set of rules based on a previous estimate of the number of people in the region of the enclosed space.
The method for estimating the number of people in a room (where sensor data is analyzed using a single occupancy criterion to group devices and estimate population) improves its accuracy by adjusting the single occupancy criterion rules based on previous population estimates.
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March 7, 2016
April 11, 2017
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